1. Technical Field of the Invention
The present invention relates to a method of erasing repeated patterns and to a pattern defect detection device in image processing when detecting pattern defects contained in electronic equipment devices such as liquid crystal panels, plasma display panels, or semiconductor wafers.
2. Description of Related Art
Conventionally, inspection of pattern defects in the manufacture of electronic equipment devices such as liquid crystal panels, plasma or display panels or semiconductor wafers was performed by visual inspection by human beings or by automatic image processing carried out by machine.
In the case of visual inspection, alterations in the type of electronic equipment being inspected can easily be coped with, and start-up is rapid, but there are the drawbacks that identifying the precise positions of defects takes time and throughput is poor. Maintaining and standardizing detection sensitivity is also a problem.
In contrast, in the case of image processing using a machine, although there are the advantages that rapid identification of the precise positions of defects and maintenance and standardization of the detection sensitivity can be achieved, there was the problem that considerable time was required for adjustment in the event of alterations in the type of electronic equipment being manufactured.
With increases in the structural fineness and performance of components in recent years, the poor throughput of visual inspection has become increasingly prominent. There are therefore considerable expectations in regard to improvement of performance of machines that perform image processing.
However, although the structural fineness and performance of electronic equipment devices such as liquid crystal panels, plasma or display panels and semiconductor wafers has considerably increased, these devices are often formed with a large number of repeated patterns identical to a partial pattern. To detect defects in such repeated patterns, conventionally, processing was performed as follows.
Firstly, pattern erasure is performed as described below by performing the processing:gout=gin−g(in+size)+offset on all the pixels of the raw image, including the imaged repetition pattern, that are within the processing region, with the standard pitch of the repetition pattern. Here, gout is the density of the pixels of the image after processing, gin is the density of the pixels of the raw image, g (in+size) is the density of a pixel separated by the standard pitch from the pixel chosen as the origin of the raw image, and offset is the density that is added as a reference density in the image after processing; in the case of 8-bit 256 gradations, this is usually the central 128 gradations. This processing is called pattern erasure processing; the image obtained by this processing is called the background image or image after pattern erasure processing.
Secondly, pixels whose density differs considerably from the background density of the background image are detected as defects. This processing is called defect detection processing.
The description will now be continued with reference to FIG. 5A and FIG. 5B. FIG. 5A shows the raw image prior to pattern erasure processing, in which an elongate pattern is repeated. FIG. 5B shows the image after pattern erasure processing. The 21 pixels that are closest to the pattern pitch are taken as constituting the size in the expression given above for pattern erasure processing. Processing is performed in the range of processing region 50 illustrated in FIG. 5A.
Also, at the bottom of FIG. 5A and FIG. 5B, there are shown the density profiles 53 and 54 on the check lines 51, 52 respectively on the raw image prior to pattern processing and the image after processing. The direction of increased brightness is the direction of approach to gradation number 255; the direction of decreasing brightness is the direction approaching gradation 0. Whether or not the pattern has been erased after processing can be ascertained by comparing the density profiles 53 and 54.
Defect detection processing consists detecting as a defect satisfaction of certain density conditions in the image after processing illustrated in FIG. 5B. Taking as an example the density profile 54 on check line 52, if a density gradation of more than the specific density gradation 135 is deemed to constitute a white defect and a density gradation of less than specific density gradation of 120 is deemed to constitute a black defect, 55 is detected as a black defect.
However, in the above conventional pattern erasure processing, there are the following three problems.
Firstly, normal portions of the pattern are left in the background image.
Secondly, although they might originally be white defects or black defects, when both white defects and black defects occur in the background image, it becomes difficult to distinguish which kind of defects they originally were.
Thirdly, processing of the peripheral pattern cannot be performed normally.
These are now described in detail below.
The first problem does not arise if the pattern pitch is an integer at all locations. However, it is impossible for the pitch to be the same over the entire raw image.
This is because, when image pickup is effected through a large number of lenses employed for image input, due to the effects of lens aberration, image pickup cannot be effected at exactly the same pitch in the center and periphery of the lenses. Also, it is difficult to make this an integer value with no error at all. Examples are the residual portions 58 and 59 of FIG. 5B.
The second problem is a phenomenon that may occur due to comparison before and after. The white defect 56 in the middle of the processing region 50 of FIG. 5A appears as defect portions 56 and 57 in FIG. 5B which are of higher density and lower density than the background density. The distance between pixel 56 and pixel 57 is of course the size of the pattern erasure processing. It is therefore impossible to tell simply from an individual defect portion, whether the original defect was a white defect or a black defect.
The third problem, like the second problem is a phenomenon that may occur due to comparison before and after. When pattern erasure processing is performed over the entire input image, as shown in FIG. 6A and FIG. 6B, a region 60 corresponding to the size of the pattern erasure processing on the right hand side of the region cannot be obtained as a result of normal processing. This is because there are no comparison pixels.